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Applied Sciences
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AI and IoT in Manufacturing
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AI and IoT in Manufacturing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 7353

Special Issue Editor

Prof. Dr. Krzysztof Koszela

E-Mail Website1 Website2
Guest Editor
Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: artificial intelligence; neural networks; machine learning; computer image analysis; computer engineering; prediction; production process optimization and modeling; process management; Internet of Things; business management; management process; trends in management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence, wireless connection, automatization, biotechnology, nanotechnology, big data, and autonomous cars are just a taste of what is still ahead of us. It remains, however, difficult to predict what the future will look like after Industry 4.0. Robots and artificial intelligence will change the way we work. By 2030, as many as 800 million workplaces and up to 75% of professions will be affected. Ultimately, robots can replace one fifth of employees hired on a full-time basis. At the same time, investment in new technologies is vital for companies—as many as half of the biggest companies in the world have initiated the implementation of automatization processes with the use of robots. According to various data, a couple of factors are accelerating the development of cognitive technologies and robotics. The first factor is the growing amount of data that companies have to process. Next is the development of the Internet and the capabilities of computing clouds, meaning that companies are more present in the digital sphere. Additionally, emerging machine learning algorithms allow the use of robots in new roles. All this has resulted in increased spending dynamics related to the implementation of artificial intelligence (AI) systems in the years 2017–2021. This is expected to reach 50%, which would mean spendings of 200 billion dollars altogether. Modern technologies with artificial intelligence are widely used in the processing industry and serve, among other things, to boost efficiency and automatization of production processes, which in turn allows for increased productivity of companies while at the same increasing their competitiveness.

The objective of this Special Issue is to present the latest advances and developments including new systems, tools, methods, and techniques to dedicated the application of manufacturing

Prof. Dr. Krzysztof Koszela
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial Intelligence (AI);
  • Intelligent manufacturing;
  • Big data;
  • Industry 4.0;
  • Internet of Things;
  • Manufacturing systems;
  • Cloud manufacturing;
  • Manufacturing processes;
  • Artificial vision;
  • Management in new digitally powered manufacturing concepts

Published Papers (3 papers)

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Research

12 pages, 9722 KiB  
Article
Beacon in Information System as Way of Supporting Identification of Cattle Behavior
by Krzysztof Koszela, Wojciech Mueller, Jakub Otrząsek, Mateusz Łukomski and Sebastian Kujawa
Appl. Sci. 2021, 11(3), 1062; https://doi.org/10.3390/app11031062 - 25 Jan 2021
Cited by 2 | Viewed by 1721
Abstract
The paper concentrates on researching the possibilities of using modern information technologies in animal production in order to monitor and identify behavior and well-being of cows. Having in mind the challenges related to managing dairy herds, and economic pressure put on breeders (as [...] Read more.
The paper concentrates on researching the possibilities of using modern information technologies in animal production in order to monitor and identify behavior and well-being of cows. Having in mind the challenges related to managing dairy herds, and economic pressure put on breeders (as well as the broadly defined well-being of animals), an endeavor was made to create a new method, which would be competitive in comparison with the existing solutions. The proposed method of collecting data and data processing with beacon devices as well as data warehouse, allows—according to the authors—a more complete identification of behaviors and physiological condition of a dairy herd. It is also worth pointing out that this method is competitive in terms of price. By virtue of the multitude of data that were collected, a decision was made to resign from processing data on a local computer and use a cloud compute engine instead. The presented information system creates a sequence of components, which were subject to verification both on the level of creating and conducting research. Research results that were received were then compared with knowledge presented in the literature. A vital element of validation of the aforementioned methodology was comparing results that were achieved in the course of research work with the system making use of pedometer. The aim of the authors was to develop a new information technology solution, as well as a method based on beacons, which are rather universal devices, with the use of data warehouses, allowing the identification of behavior and physiological state of milk cattle, the method which would be competitive in comparison with the existing solutions, especially in terms of price. In the proposed solution, both information coming from microcomputers and weather forecast data coming from weather forecast stations, which make the above identification easy, were used as data sources. Full article
(This article belongs to the Special Issue AI and IoT in Manufacturing)
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12 pages, 4527 KiB  
Article
Simulation Verification of the Contact Parameter Influence on the Forces’ Course of Cereal Grain Impact against a Stiff Surface
by Włodzimierz Kęska, Jacek Marcinkiewicz, Łukasz Gierz, Żaneta Staszak, Jarosław Selech and Krzysztof Koszela
Appl. Sci. 2021, 11(2), 466; https://doi.org/10.3390/app11020466 - 06 Jan 2021
Cited by 2 | Viewed by 1822
Abstract
The continuous development of computer technology has made it applicable in many scientific fields, including research into a wide range of processes in agricultural machines. It allows the simulation of very complex physical phenomena, including grain motion. A recently discovered discrete element method [...] Read more.
The continuous development of computer technology has made it applicable in many scientific fields, including research into a wide range of processes in agricultural machines. It allows the simulation of very complex physical phenomena, including grain motion. A recently discovered discrete element method (DEM) is used for this purpose. It involves direct integration of equations of grain system motion under the action of various forces, the most important of which are contact forces. The method’s accuracy depends mainly on precisely developed mathematical models of contacts. The creation of such models requires empirical validation, an experiment that investigates the course of contact forces at the moment of the impact of the grains. To achieve this, specialised test stations equipped with force and speed sensors were developed. The correct selection of testing equipment and interpretation of results play a decisive role in this type of research. This paper focuses on the evaluation of the force sensor dynamic properties’ influence on the measurement accuracy of the course of the plant grain impact forces against a stiff surface. The issue was examined using the computer simulation method. A proprietary computer software with the main calculation module and data input procedures, which presents results in a graphic form, was used for calculations. From the simulation, graphs of the contact force and force signal from the sensor were obtained. This helped to clearly indicate the essence of the correct selection of parameters used in the tests of sensors, which should be characterised by high resonance frequency. Full article
(This article belongs to the Special Issue AI and IoT in Manufacturing)
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18 pages, 71387 KiB  
Article
Computer Aided Modeling of Wood Chips Transport by Means of a Belt Conveyor with Use of Discrete Element Method
by Łukasz Gierz, Łukasz Warguła, Mateusz Kukla, Krzysztof Koszela and Tomasz Szymon Zwiachel
Appl. Sci. 2020, 10(24), 9091; https://doi.org/10.3390/app10249091 - 18 Dec 2020
Cited by 15 | Viewed by 3078
Abstract
The effectiveness and precision of transporting wood chips on the transport trailer or hopper depends on an inclination angle, a conveyor belt speed, and length. In order to devise a methodology aiding designing and the selection of technical and performance parameters (aiding the [...] Read more.
The effectiveness and precision of transporting wood chips on the transport trailer or hopper depends on an inclination angle, a conveyor belt speed, and length. In order to devise a methodology aiding designing and the selection of technical and performance parameters (aiding the settings of conveyor belt sub-assemblies), the authors carried out the simulation tests concerning wood chips transport on the belt conveyor and their outlet. For the purposes of these tests, a simulation model was performed in the Rocky DEM (discrete element method) software in the numerical analysis environment and compared to analytical tests. The tested wood chips were taken from cherry plum branches chipping processes (Prunus cerasifera Ehrh. Beitr. Naturk. 4:17. 1789 (Gartenkalender 4:189-204. 1784)), out of which seven basic fractions were separated, which differed mainly in terms of their diameter from 5 mm to 50 mm and the length of 150 mm. The article presents the results of wood chips ejection distance in the form of the 3D functions of wood chips ejection distance depending on the conveyor belt inclination angle and belt speed. The results are presented for five conveyor belt lengths (1 m, 2 m, 3 m, 4 m, 5 m). The tests also involved the conveyor belt inclination angle in the range from 10° to 50° and the belt velocity in the range from 1 m/s2 to 5 m/s2. The numerical test results demonstrate higher average values of wood chips ejection distance than designated in the analytical model. The average arithmetical difference in the results between the numerical and analytical model is at the level of 13%. Full article
(This article belongs to the Special Issue AI and IoT in Manufacturing)
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